Lutfidts/DiabetesPrediciton_

1import streamlit as st2import pandas as pd3import numpy as np4import joblib5import base646 7from keras.models import load_model8 9 10import warnings11warnings.filterwarnings("ignore")12 13 14st.markdown("<h2 style='text-align: center;'>Diabetes Prediction</h2>", unsafe_allow_html=True)15st.markdown('---'*10)16 17model_final = joblib.load('sklearn_pipeline.pkl')18model_final.named_steps['modeling'].model = load_model('model_keras.h5')19 20pilihan = st.selectbox('Apa yang ingin Anda lakukan?',['Prediksi dari file excel','Input Manual'])21 22if pilihan == 'Prediksi dari file excel':23    def set_bg_1(main_bg):24 25        main_bg_ext = "jpg"26            27        st.markdown(28             f"""29             <style>30             .stApp {{31                 background: url(data:image/{main_bg_ext};base64,{base64.b64encode(open(main_bg, "rb").read()).decode()});32                 background-position: center;33                 background-size: 720px 520px;34                 background-repeat: no-repeat35             }}36             </style>37             """,38             unsafe_allow_html=True39         )40    set_bg_1('diabtescheck.jpg')41    42    # Mengupload file43    upload_file = st.file_uploader('Pilih file excel', type='xlsx')44    if upload_file is not None:45        dataku1 = pd.read_excel(upload_file)46        dataku1.rename(columns={"concave points_mean":"concave_points_mean","concave points_se":"concave_points_se","concave points_worst":"concave_points_worst"}, inplace=True)47        dataku = dataku1.copy()48        dataku.drop(['id'],axis=1,inplace=True)49        #dataku.drop(columns='diagnosis',axis=0,inplace=True)50        st.write(dataku1)51        st.success('File berhasil diupload')52        if st.button('Diabetes Prediction'):53            hasil = model_final.predict(dataku)54            #st.write('Prediksi',hasil)55            # Keputusan56            for i in range(len(hasil)):57                if hasil[i] == 1:58                    st.write("ID ",dataku1['id'][i]," diprediksi Yes")59                else:60                    st.write("ID ",dataku1['id'][i]," diprediksi No")61    else:62        st.error('File yang diupload kosong, silakan pilih file yang valid')63        #st.markdown('File yang diupload kosong, silakan pilih file yang valid')64else:65    def set_bg_2(main_bg):66 67        main_bg_ext = "jpg"68            69        st.markdown(70             f"""71             <style>72             .stApp {{73                 background: url(data:image/{main_bg_ext};base64,{base64.b64encode(open(main_bg, "rb").read()).decode()});74                 background-position: center;75                 background-size: 720px 520px;76                 background-repeat: no-repeat77             }}78             </style>79             """,80             unsafe_allow_html=True81         )82    set_bg_2('diabetic.jpg')83    84    #185    with st.container():86        col1, col2 = st.columns(2)87        with col1:88            Pregnancies = st.number_input('Pregnancies', value=0.41)89        with col2:90            PlasmaGlucose = st.number_input('PlasmaGlucose', value=0.75)91    92    #293    with st.container():94        col1, col2 = st.columns(2)95        with col1:96            DiastolicBloodPressure = st.number_input('DiastolicBloodPressure', value=0.63)97        with col2:98            TricepsThickness = st.number_input('TricepsThickness', value=0.29)99    100    #3101    with st.container():102        col1, col2 = st.columns(2)103        with col1:104            SerumInsulin = st.number_input('SerumInsulin', value=0.14)105        with col2:106            BMI = st.number_input('BMI', value=0.52)107    108    #4109    with st.container():110        col1, col2 = st.columns(2)111        with col1:112            DiabetesPedigree = st.number_input('DiabetesPedigree', value=0.26)113        with col2:114            Age = st.number_input('Age', value=0.55)115    116        117    # Inference118    data = {119            'Pregnancies': Pregnancies,120            'PlasmaGlucose': PlasmaGlucose,121            'DiastolicBloodPressure': DiastolicBloodPressure,122            'TricepsThickness': TricepsThickness,123            'SerumInsulin': SerumInsulin,124            'BMI': BMI,125            'DiabetesPedigree': DiabetesPedigree,126            'Age': Age,    127            }128    129    130    # Tabel data131    kolom = list(data.keys())132    df = pd.DataFrame([data.values()], columns=kolom)133    134    mystyle = '''135    <style>136        p {137            text-align: center;138            font-weight: bold;139            font-weight: 100px140        }141    </style>142    '''143    144    # Memunculkan hasil di Web 145    st.write('***'*10)146    if st.button('Breast Cancer Classification'):147        prediksi = model_final.predict(df)148        if (prediksi[0] == 1):149            st.write(mystyle,'1',unsafe_allow_html=True)150        else:151            st.write(mystyle,'0',unsafe_allow_html=True)152